Author manuscript, published in "(POMA- ICA 2013), Montreal: Canada (2013)" An invariant convolution model and its Variational Bayesian Approximation approach via Students-t priors for acoustic imaging in colored noises
Bibliographic record
Abstract
Acoustic imaging is a powerful tool to localize and reconstruct source powers using microphone array. However, it often involves the ill-posed inversions and becomes too time-consuming to obtain high spatial resolutions. In this paper, we firstly propose a shift-invariant convolution model to approximate the forward model of acoustic power propagation. The convolution kernel is derived from the Symmetric Toepliz Block Toepliz (STBT) structure of propagation matrix. Then we propose a hierarchical Bayesian inference approach via Variational Bayesian Approximation (VBA) criterion in order to achieve robust acoustic imaging in colored background noises. For super spatial resolution and wide dynamic power range, we explore the Student’s-t prior on the acoustic power distribution thanks to the sparsity and heavy tail of prior model. Colored noise distributions are also modeled by the Student’s-t prior, and this does not excessively penalize large model errors as the Gaussian white prior does. Finally proposed 2D convolution model and VBA approach are validated through simulations and real data from wind tunnel compared to classical methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".